ArticleNPJ systems biology and applications2024
SignalingProfiler 2.0 a network-based approach to bridge multi-omics data to phenotypic hallmarks.
Article in NPJ systems biology and applications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Causal network analysis of omics data using prior knowledge databases.Briefings in bioinformatics · 2025Pooled it
- Cardiac proteomic and phosphoproteomic profiling defines clinically relevant molecular subgroups in human heart failure.Nature cardiovascular research · 2026Article
- MultiOmicsXplorer, a tool to browse, access and analyse multi-omics data.BMC bioinformatics · 2026Article
- PatientProfiler: building patient-specific signaling models from proteogenomic data.Molecular systems biology · 2025Article
- NeKo: A tool for automatic network construction from prior knowledge.PLoS computational biology · 2025Article
- The evolution of analytical techniques for multiplex analysis of protein biomarkers.Expert review of proteomics · 2025Review
- NetworkCommons: bridging data, knowledge, and methods to build and evaluate context-specific biological networks.Bioinformatics (Oxford, England) · 2025Article
- Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Unraveling how cellular signaling is remodeled upon perturbation is crucial for understanding disease mechanisms and identifying potential drug targets. In this pursuit, computational tools generating mechanistic hypotheses from multi-omics data have invaluable potential. Here, we present a newly implemented version (2.0) of SignalingProfiler, a multi-step pipeline to draw mechanistic hypotheses on the signaling events impacting cellular phenotypes. SignalingProfiler 2.0 derives context-specific signaling networks by integrating proteogenomic data with the prior knowledge-causal network. This is a freely accessible and flexible tool that incorporates statistical, footprint-based, and graph algorithms to accelerate the integration and interpretation of multi-omics data. Through a benchmarking process on three proof-of-concept studies, we demonstrate the tool's ability to generate hierarchical mechanistic networks recapitulating novel and known perturbed signaling and phenotypic outcomes, in both human and mice contexts. In summary, SignalingProfiler 2.0 addresses the emergent need to derive biologically relevant information from complex multi-omics data by extracting interpretable networks.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.